{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "973a5ce0-f98a-4d71-9520-eefd83c9d95f",
   "metadata": {},
   "source": [
    "# 神经网络NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5975736c-aa8f-4a8e-b588-ff409b21a538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据\n",
    "import pandas as pd\n",
    "wine = pd.read_csv('wine.txt', names = [\"Cultivator\", \"Alchol\", \"Malic_Acid\", \"Ash\", \"Alcalinity_of_Ash\", \"Magnesium\", \"Total_phenols\", \"Falvanoids\", \"Nonflavanoid_phenols\", \"Proanthocyanins\", \"Color_intensity\", \"Hue\", \"OD280\", \"Proline\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "487c794e-921c-4e76-befa-f38bfadd6f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cultivator</th>\n",
       "      <th>Alchol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity_of_Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_phenols</th>\n",
       "      <th>Falvanoids</th>\n",
       "      <th>Nonflavanoid_phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cultivator  Alchol  Malic_Acid   Ash  Alcalinity_of_Ash  Magnesium  \\\n",
       "0           1   14.23        1.71  2.43               15.6        127   \n",
       "1           1   13.20        1.78  2.14               11.2        100   \n",
       "2           1   13.16        2.36  2.67               18.6        101   \n",
       "3           1   14.37        1.95  2.50               16.8        113   \n",
       "4           1   13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total_phenols  Falvanoids  Nonflavanoid_phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color_intensity   Hue  OD280  Proline  \n",
       "0             5.64  1.04   3.92     1065  \n",
       "1             4.38  1.05   3.40     1050  \n",
       "2             5.68  1.03   3.17     1185  \n",
       "3             7.80  0.86   3.45     1480  \n",
       "4             4.32  1.04   2.93      735  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ebbaa5ce-55c3-48c5-8b85-cc809485c4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 14)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cd2d678-1be4-40c0-9281-3315a4a4ecd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine.drop('Cultivator',axis=1)\n",
    "y = wine['Cultivator']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09af1962-c0c4-40ec-9a78-b586dcecf591",
   "metadata": {},
   "source": [
    "# 鸢尾花"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2f30b090-6b01-47a9-b030-299f9f101b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('D:/work/machine-learning/notebooks/PCA/iris.csv')\n",
    "\n",
    "# 处理标签列\n",
    "data['Species'] = data['Species'].str.split(': ').str[0]\n",
    "\n",
    "# 准备数据\n",
    "X = data.iloc[:, 0:4].values  # 特征列\n",
    "y = data['Species'].values   # 标签列\n",
    "\n",
    "# 将类别标签转换为整数编码\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=4096, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a772a601-5f0b-41f7-aaf2-77f765cd5fdb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9331427d-5617-4ce6-b3df-b315402efc62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "076a6145-a14c-41e0-b475-dfb34000fcd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c5dfe700-2f5b-4b45-9daf-03bfcc7a8d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据：\n",
      " [[143.    5.8   2.7   5.1]\n",
      " [ 98.    6.2   2.9   4.3]\n",
      " [ 53.    6.9   3.1   4.9]\n",
      " [ 39.    4.4   3.    1.3]\n",
      " [ 45.    5.1   3.8   1.9]\n",
      " [ 94.    5.    2.3   3.3]\n",
      " [ 62.    5.9   3.    4.2]\n",
      " [103.    7.1   3.    5.9]\n",
      " [123.    7.7   2.8   6.7]\n",
      " [ 61.    5.    2.    3.5]\n",
      " [ 96.    5.7   3.    4.2]\n",
      " [ 64.    6.1   2.9   4.7]\n",
      " [ 95.    5.6   2.7   4.2]\n",
      " [ 28.    5.2   3.5   1.5]\n",
      " [ 60.    5.2   2.7   3.9]\n",
      " [118.    7.7   3.8   6.7]\n",
      " [104.    6.3   2.9   5.6]\n",
      " [  6.    5.4   3.9   1.7]\n",
      " [117.    6.5   3.    5.5]\n",
      " [137.    6.3   3.4   5.6]\n",
      " [ 44.    5.    3.5   1.6]\n",
      " [128.    6.1   3.    4.9]\n",
      " [ 20.    5.1   3.8   1.5]\n",
      " [112.    6.4   2.7   5.3]\n",
      " [  4.    4.6   3.1   1.5]\n",
      " [135.    6.1   2.6   5.6]\n",
      " [ 55.    6.5   2.8   4.6]\n",
      " [132.    7.9   3.8   6.4]\n",
      " [ 99.    5.1   2.5   3. ]\n",
      " [105.    6.5   3.    5.8]\n",
      " [129.    6.4   2.8   5.6]\n",
      " [ 47.    5.1   3.8   1.6]\n",
      " [150.    5.9   3.    5.1]\n",
      " [139.    6.    3.    4.8]\n",
      " [ 67.    5.6   3.    4.5]\n",
      " [ 85.    5.4   3.    4.5]\n",
      " [ 33.    5.2   4.1   1.5]\n",
      " [ 74.    6.1   2.8   4.7]\n",
      " [120.    6.    2.2   5. ]\n",
      " [ 90.    5.5   2.5   4. ]\n",
      " [ 88.    6.3   2.3   4.4]\n",
      " [ 10.    4.9   3.1   1.5]\n",
      " [ 79.    6.    2.9   4.5]\n",
      " [ 82.    5.5   2.4   3.7]\n",
      " [ 49.    5.3   3.7   1.5]\n",
      " [ 78.    6.7   3.    5. ]\n",
      " [108.    7.3   2.9   6.3]\n",
      " [131.    7.4   2.8   6.1]\n",
      " [ 42.    4.5   2.3   1.3]\n",
      " [100.    5.7   2.8   4.1]\n",
      " [ 34.    5.5   4.2   1.4]\n",
      " [ 65.    5.6   2.9   3.6]\n",
      " [ 13.    4.8   3.    1.4]\n",
      " [122.    5.6   2.8   4.9]\n",
      " [106.    7.6   3.    6.6]\n",
      " [ 73.    6.3   2.5   4.9]\n",
      " [140.    6.9   3.1   5.4]\n",
      " [ 52.    6.4   3.2   4.5]\n",
      " [ 36.    5.    3.2   1.2]\n",
      " [  8.    5.    3.4   1.5]\n",
      " [  1.    5.1   3.5   1.4]\n",
      " [  9.    4.4   2.9   1.4]\n",
      " [107.    4.9   2.5   4.5]\n",
      " [145.    6.7   3.3   5.7]\n",
      " [ 21.    5.4   3.4   1.7]\n",
      " [ 12.    4.8   3.4   1.6]\n",
      " [134.    6.3   2.8   5.1]\n",
      " [ 29.    5.2   3.4   1.4]\n",
      " [ 19.    5.7   3.8   1.7]\n",
      " [121.    6.9   3.2   5.7]\n",
      " [102.    5.8   2.7   5.1]\n",
      " [146.    6.7   3.    5.2]\n",
      " [  2.    4.9   3.    1.4]\n",
      " [ 83.    5.8   2.7   3.9]\n",
      " [ 51.    7.    3.2   4.7]\n",
      " [119.    7.7   2.6   6.9]\n",
      " [ 23.    4.6   3.6   1. ]\n",
      " [130.    7.2   3.    5.8]\n",
      " [ 57.    6.3   3.3   4.7]\n",
      " [ 40.    5.1   3.4   1.5]\n",
      " [ 97.    5.7   2.9   4.2]\n",
      " [142.    6.9   3.1   5.1]\n",
      " [111.    6.5   3.2   5.1]\n",
      " [ 56.    5.7   2.8   4.5]\n",
      " [ 66.    6.7   3.1   4.4]\n",
      " [  3.    4.7   3.2   1.3]\n",
      " [ 14.    4.3   3.    1.1]\n",
      " [ 59.    6.6   2.9   4.6]\n",
      " [ 86.    6.    3.4   4.5]\n",
      " [ 11.    5.4   3.7   1.5]\n",
      " [ 22.    5.1   3.7   1.5]\n",
      " [ 91.    5.5   2.6   4.4]\n",
      " [ 35.    4.9   3.1   1.5]\n",
      " [ 63.    6.    2.2   4. ]\n",
      " [ 72.    6.1   2.8   4. ]\n",
      " [ 84.    6.    2.7   5.1]\n",
      " [ 50.    5.    3.3   1.4]\n",
      " [ 16.    5.7   4.4   1.5]\n",
      " [144.    6.8   3.2   5.9]\n",
      " [110.    7.2   3.6   6.1]\n",
      " [113.    6.8   3.    5.5]\n",
      " [ 32.    5.4   3.4   1.5]\n",
      " [133.    6.4   2.8   5.6]\n",
      " [ 17.    5.4   3.9   1.3]\n",
      " [ 30.    4.7   3.2   1.6]]\n",
      "均值： [74.64761905  5.86571429  3.07428571  3.78666667]\n",
      "标准差： [43.62545804  0.84671112  0.4484956   1.78496721]\n",
      "标准化后的数据：\n",
      " StandardScaler()\n"
     ]
    }
   ],
   "source": [
    "# Fit only to the training data\n",
    "X_scaled = scaler.fit(X_train)\n",
    "print(\"原始数据：\\n\", X_train)\n",
    "print(\"均值：\", scaler.mean_)\n",
    "print(\"标准差：\", scaler.scale_)\n",
    "print(\"标准化后的数据：\\n\", X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ed57cf19-4cbf-427c-bcda-e4f3e890604b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.5668003 , -0.07761122, -0.83453599,  0.73577449],\n",
       "       [ 0.53529251,  0.39480492, -0.38860073,  0.28758698],\n",
       "       [-0.49621528,  1.22153316,  0.05733453,  0.62372761],\n",
       "       [-0.81712882, -1.73106771, -0.1656331 , -1.39311616],\n",
       "       [-0.67959445, -0.90433947,  1.61810794, -1.05697553],\n",
       "       [ 0.44360293, -1.0224435 , -1.72640651, -0.2726474 ],\n",
       "       [-0.28991373,  0.04049281, -0.1656331 ,  0.23156354],\n",
       "       [ 0.64990449,  1.45774123, -0.1656331 ,  1.18396199],\n",
       "       [ 1.1083524 ,  2.16636544, -0.61156836,  1.6321495 ],\n",
       "       [-0.31283612, -1.0224435 , -2.3953094 , -0.16060052],\n",
       "       [ 0.48944772, -0.19571526, -0.1656331 ,  0.23156354],\n",
       "       [-0.24406893,  0.27670088, -0.38860073,  0.51168073],\n",
       "       [ 0.46652532, -0.31381929, -0.83453599,  0.23156354],\n",
       "       [-1.06927517, -0.78623543,  0.94920505, -1.28106929],\n",
       "       [-0.33575852, -0.78623543, -0.83453599,  0.06349323],\n",
       "       [ 0.99374042,  2.16636544,  1.61810794,  1.6321495 ],\n",
       "       [ 0.67282688,  0.51290895, -0.38860073,  1.01589168],\n",
       "       [-1.57356787, -0.55002736,  1.84107557, -1.16902241],\n",
       "       [ 0.97081802,  0.74911702, -0.1656331 ,  0.95986824],\n",
       "       [ 1.42926593,  0.51290895,  0.72623742,  1.01589168],\n",
       "       [-0.70251684, -1.0224435 ,  0.94920505, -1.22504585],\n",
       "       [ 1.22296437,  0.27670088, -0.1656331 ,  0.62372761],\n",
       "       [-1.25265433, -0.90433947,  1.61810794, -1.28106929],\n",
       "       [ 0.85620605,  0.63101299, -0.83453599,  0.84782136],\n",
       "       [-1.61941266, -1.49485964,  0.05733453, -1.28106929],\n",
       "       [ 1.38342114,  0.27670088, -1.05750362,  1.01589168],\n",
       "       [-0.45037049,  0.74911702, -0.61156836,  0.4556573 ],\n",
       "       [ 1.31465395,  2.40257351,  1.61810794,  1.46407918],\n",
       "       [ 0.55821491, -0.90433947, -1.28047125, -0.44071771],\n",
       "       [ 0.69574928,  0.74911702, -0.1656331 ,  1.12793855],\n",
       "       [ 1.24588677,  0.63101299, -0.61156836,  1.01589168],\n",
       "       [-0.63374966, -0.90433947,  1.61810794, -1.22504585],\n",
       "       [ 1.72725707,  0.04049281, -0.1656331 ,  0.73577449],\n",
       "       [ 1.47511072,  0.15859685, -0.1656331 ,  0.56770417],\n",
       "       [-0.17530175, -0.31381929, -0.1656331 ,  0.39963386],\n",
       "       [ 0.23730137, -0.55002736, -0.1656331 ,  0.39963386],\n",
       "       [-0.95466319, -0.78623543,  2.28701083, -1.28106929],\n",
       "       [-0.01484498,  0.27670088, -0.61156836,  0.51168073],\n",
       "       [ 1.03958521,  0.15859685, -1.94937414,  0.67975105],\n",
       "       [ 0.35191335, -0.43192333, -1.28047125,  0.11951667],\n",
       "       [ 0.30606856,  0.51290895, -1.72640651,  0.34361042],\n",
       "       [-1.48187829, -1.14054754,  0.05733453, -1.28106929],\n",
       "       [ 0.099767  ,  0.15859685, -0.38860073,  0.39963386],\n",
       "       [ 0.16853418, -0.43192333, -1.50343888, -0.04855365],\n",
       "       [-0.58790487, -0.6681314 ,  1.39514031, -1.28106929],\n",
       "       [ 0.0768446 ,  0.98532509, -0.1656331 ,  0.67975105],\n",
       "       [ 0.76451646,  1.6939493 , -0.38860073,  1.40805575],\n",
       "       [ 1.29173156,  1.81205333, -0.61156836,  1.29600887],\n",
       "       [-0.74836163, -1.61296367, -1.72640651, -1.39311616],\n",
       "       [ 0.5811373 , -0.19571526, -0.61156836,  0.17554011],\n",
       "       [-0.9317408 , -0.43192333,  2.50997846, -1.33709272],\n",
       "       [-0.22114654, -0.31381929, -0.38860073, -0.10457708],\n",
       "       [-1.4131111 , -1.25865157, -0.1656331 , -1.33709272],\n",
       "       [ 1.08543   , -0.31381929, -0.61156836,  0.62372761],\n",
       "       [ 0.71867167,  2.0482614 , -0.1656331 ,  1.57612606],\n",
       "       [-0.03776738,  0.51290895, -1.28047125,  0.62372761],\n",
       "       [ 1.49803312,  1.22153316,  0.05733453,  0.9038448 ],\n",
       "       [-0.51913768,  0.63101299,  0.28030216,  0.39963386],\n",
       "       [-0.88589601, -1.0224435 ,  0.28030216, -1.4491396 ],\n",
       "       [-1.52772308, -1.0224435 ,  0.72623742, -1.28106929],\n",
       "       [-1.68817985, -0.90433947,  0.94920505, -1.33709272],\n",
       "       [-1.50480068, -1.73106771, -0.38860073, -1.33709272],\n",
       "       [ 0.74159407, -1.14054754, -1.28047125,  0.39963386],\n",
       "       [ 1.6126451 ,  0.98532509,  0.50326979,  1.07191512],\n",
       "       [-1.22973194, -0.55002736,  0.72623742, -1.16902241],\n",
       "       [-1.4360335 , -1.25865157,  0.72623742, -1.22504585],\n",
       "       [ 1.36049875,  0.51290895, -0.61156836,  0.73577449],\n",
       "       [-1.04635277, -0.78623543,  0.72623742, -1.33709272],\n",
       "       [-1.27557673, -0.19571526,  1.61810794, -1.16902241],\n",
       "       [ 1.06250761,  1.22153316,  0.28030216,  1.07191512],\n",
       "       [ 0.62698209, -0.07761122, -0.83453599,  0.73577449],\n",
       "       [ 1.63556749,  0.98532509, -0.1656331 ,  0.79179793],\n",
       "       [-1.66525745, -1.14054754, -0.1656331 , -1.33709272],\n",
       "       [ 0.19145658, -0.07761122, -0.83453599,  0.06349323],\n",
       "       [-0.54206007,  1.33963719,  0.28030216,  0.51168073],\n",
       "       [ 1.01666281,  2.16636544, -1.05750362,  1.74419637],\n",
       "       [-1.18388715, -1.49485964,  1.17217268, -1.56118648],\n",
       "       [ 1.26880916,  1.57584526, -0.1656331 ,  1.12793855],\n",
       "       [-0.4045257 ,  0.51290895,  0.50326979,  0.51168073],\n",
       "       [-0.79420642, -0.90433947,  0.72623742, -1.28106929],\n",
       "       [ 0.51237011, -0.19571526, -0.38860073,  0.23156354],\n",
       "       [ 1.54387791,  1.22153316,  0.05733453,  0.73577449],\n",
       "       [ 0.83328365,  0.74911702,  0.28030216,  0.73577449],\n",
       "       [-0.4274481 , -0.19571526, -0.61156836,  0.39963386],\n",
       "       [-0.19822414,  0.98532509,  0.05733453,  0.34361042],\n",
       "       [-1.64233506, -1.3767556 ,  0.28030216, -1.39311616],\n",
       "       [-1.39018871, -1.84917174, -0.1656331 , -1.50516304],\n",
       "       [-0.35868091,  0.86722105, -0.38860073,  0.4556573 ],\n",
       "       [ 0.26022377,  0.15859685,  0.72623742,  0.39963386],\n",
       "       [-1.45895589, -0.55002736,  1.39514031, -1.28106929],\n",
       "       [-1.20680954, -0.90433947,  1.39514031, -1.28106929],\n",
       "       [ 0.37483574, -0.43192333, -1.05750362,  0.34361042],\n",
       "       [-0.9088184 , -1.14054754,  0.05733453, -1.28106929],\n",
       "       [-0.26699133,  0.15859685, -1.94937414,  0.11951667],\n",
       "       [-0.06068977,  0.27670088, -0.61156836,  0.11951667],\n",
       "       [ 0.21437897,  0.15859685, -0.83453599,  0.73577449],\n",
       "       [-0.56498247, -1.0224435 ,  0.50326979, -1.33709272],\n",
       "       [-1.34434391, -0.19571526,  2.95591372, -1.28106929],\n",
       "       [ 1.5897227 ,  1.10342912,  0.28030216,  1.18396199],\n",
       "       [ 0.81036126,  1.57584526,  1.17217268,  1.29600887],\n",
       "       [ 0.87912844,  1.10342912, -0.1656331 ,  0.95986824],\n",
       "       [-0.97758559, -0.55002736,  0.72623742, -1.28106929],\n",
       "       [ 1.33757635,  0.63101299, -0.61156836,  1.01589168],\n",
       "       [-1.32142152, -0.55002736,  1.84107557, -1.39311616],\n",
       "       [-1.02343038, -1.3767556 ,  0.28030216, -1.22504585]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now apply the transformations to the data:\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "402696ee-d906-4fae-8af6-b92eb1e8d39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7fdf44ac-7e1f-49c6-8b63-60dfe64c7503",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(3,3),max_iter=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "ac149861-ebb4-4939-8742-041f3c7a4f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(3, 3), max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=(3, 3), max_iter=500)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(3, 3), max_iter=500)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4bdd9a93-45cb-4196-9165-6c8e1099517b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "fecd639e-0252-47d1-a67c-ea4a60d1dba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "21fac337-fde6-42c1-8bec-4d82a8bb4166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15  0  0]\n",
      " [ 0 12  3]\n",
      " [ 0  1 14]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(y_test,predictions), display_labels=['0','1','2'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "3a02a7f0-9b88-4b99-8bfb-b7b50ed63da1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类评估汇总报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        15\n",
      "           1       0.92      0.80      0.86        15\n",
      "           2       0.82      0.93      0.88        15\n",
      "\n",
      "    accuracy                           0.91        45\n",
      "   macro avg       0.92      0.91      0.91        45\n",
      "weighted avg       0.92      0.91      0.91        45\n",
      "\n",
      "误分类矩阵:\n",
      " [[15  0  0]\n",
      " [ 0 12  3]\n",
      " [ 0  1 14]]\n"
     ]
    }
   ],
   "source": [
    "# 利用模型对测试集进行预测，输出target预测标签值和概率\n",
    "y_test_pred = mlp.predict(X_test)\n",
    "y_test_prob = mlp.predict_proba(X_test)\n",
    "# 分类评估汇总报告classification_report\n",
    "print('分类评估汇总报告:\\n',classification_report(y_test,y_test_pred))\n",
    "# 误分类矩阵 confusion_matrix\n",
    "print('误分类矩阵:\\n',confusion_matrix(y_test,y_test_pred))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773bef3b-6d1e-4e99-8641-5be1d492a25f",
   "metadata": {},
   "source": [
    "# wine 的kmeans聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ebceac90-48c3-4983-90df-3dbb6e57e92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 加载数据\n",
    "dataset = pd.read_csv('wine.txt', header=None)\n",
    "X = dataset.iloc[:, 1:].values  # 假设第一列是标签，我们只取特征\n",
    "y = dataset.iloc[:, 0].values   # 真实标签\n",
    "\n",
    "# 标准化特征\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 肘部法则寻找最优聚类个数\n",
    "wcss = []\n",
    "for i in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++')\n",
    "    kmeans.fit(X_scaled)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 10), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('No. of cluster')\n",
    "plt.ylabel('wcss: sum of dist. of sample to their closest cluster center')\n",
    "plt.show()\n",
    "\n",
    "# 根据肘部法则选择的聚类个数进行KMeans聚类\n",
    "optimal_clusters = 3  # 假设最优聚类个数为3\n",
    "kmeans_1 = KMeans(n_clusters=optimal_clusters, init='k-means++')\n",
    "kmeans_1.fit(X_scaled)\n",
    "cluster_pred = kmeans_1.predict(X_scaled)\n",
    "\n",
    "# 使用PCA降维到2维\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "# 获取质心在PCA空间中的坐标\n",
    "centers_pca = pca.transform(kmeans_1.cluster_centers_)\n",
    "\n",
    "# 可视化聚类结果\n",
    "colors = ['yellow', 'purple', 'teal']  # 定义颜色\n",
    "for i in range(optimal_clusters):\n",
    "    plt.scatter(X_pca[cluster_pred == i, 0], X_pca[cluster_pred == i, 1], color=colors[i], label=f'Cluster {i}', s=50)\n",
    "plt.scatter(centers_pca[:, 0], centers_pca[:, 1], color='red', s=100, alpha=0.75, marker='.', label='Centroids')\n",
    "plt.title('KMeans Clustering on Wine Dataset with PCA')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# 混淆矩阵\n",
    "class_labels = np.unique(y)\n",
    "cnf_matrix = confusion_matrix(y, cluster_pred)\n",
    "show_confusion_matrix(cnf_matrix, class_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7edc14c3-c15a-4ac3-865c-2bcaa5e30761",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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